WO2021080295A1 - Procédé et dispositif de conception de composé - Google Patents
Procédé et dispositif de conception de composé Download PDFInfo
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- WO2021080295A1 WO2021080295A1 PCT/KR2020/014354 KR2020014354W WO2021080295A1 WO 2021080295 A1 WO2021080295 A1 WO 2021080295A1 KR 2020014354 W KR2020014354 W KR 2020014354W WO 2021080295 A1 WO2021080295 A1 WO 2021080295A1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/10—Analysis or design of chemical reactions, syntheses or processes
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/40—Searching chemical structures or physicochemical data
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/50—Molecular design, e.g. of drugs
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/60—In silico combinatorial chemistry
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- Bioinformatics & Cheminformatics (AREA)
- Crystallography & Structural Chemistry (AREA)
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- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Analytical Chemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
Un procédé de génération d'informations de composé dans un dispositif de calcul selon un mode de réalisation de la présente invention comprend les étapes consistant à : obtenir un modèle d'apprentissage concernant des informations sur une structure partielle; obtenir des informations sur une molécule source à soumettre à une modification de structure partielle; obtenir des informations sur un ensemble de structures partielles comprenant une pluralité de structures partielles de la molécule source; sélectionner une structure partielle cible à modifier parmi les structures partielles de l'ensemble de structures partielles; obtenir des informations sur la structure partielle modifiée de la structure partielle cible à l'aide du modèle d'apprentissage; et délivrer en sortie des informations de résultat dans lesquelles la structure partielle modifiée a été appliquée à la structure partielle cible dans la molécule source.
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP20878286.2A EP4050612A4 (fr) | 2019-10-21 | 2020-10-20 | Procédé et dispositif de conception de composé |
US17/770,555 US20220383993A1 (en) | 2019-10-21 | 2020-10-20 | Method and device for designing compound |
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
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KR20190130769 | 2019-10-21 | ||
KR10-2019-0130769 | 2019-10-21 | ||
KR20200046192 | 2020-04-16 | ||
KR10-2020-0046192 | 2020-04-16 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2021080295A1 true WO2021080295A1 (fr) | 2021-04-29 |
Family
ID=75619419
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/KR2020/014354 WO2021080295A1 (fr) | 2019-10-21 | 2020-10-20 | Procédé et dispositif de conception de composé |
Country Status (4)
Country | Link |
---|---|
US (1) | US20220383993A1 (fr) |
EP (1) | EP4050612A4 (fr) |
KR (2) | KR102296188B1 (fr) |
WO (1) | WO2021080295A1 (fr) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113409898A (zh) * | 2021-06-30 | 2021-09-17 | 北京百度网讯科技有限公司 | 分子结构获取方法、装置、电子设备及存储介质 |
Families Citing this family (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210287137A1 (en) * | 2020-03-13 | 2021-09-16 | Korea University Research And Business Foundation | System for predicting optical properties of molecules based on machine learning and method thereof |
KR20230073630A (ko) * | 2021-11-19 | 2023-05-26 | 주식회사 제이엘케이바이오 | 화합물 최적화를 위한 장치 및 방법 |
WO2024063584A1 (fr) * | 2022-09-21 | 2024-03-28 | (주)신테카바이오 | Procédé d'analyse de structure de liaison ligand-protéine basé sur un vecteur d'atome central d'une nouvelle plateforme de médicament à intelligence artificielle |
WO2024063583A1 (fr) * | 2022-09-21 | 2024-03-28 | (주)신테카바이오 | Procédé de génération de dérivés à l'aide d'une structure de poche de liaison de protéine cible par l'intermédiaire d'une plateforme de découverte de médicament à intelligence artificielle (ia) |
KR20240054892A (ko) | 2022-10-19 | 2024-04-26 | 주식회사 엘지화학 | 고분자 그래프 신경망 및 그 구현 방법 |
WO2024085562A1 (fr) * | 2022-10-19 | 2024-04-25 | 주식회사 엘지화학 | Réseau neuronal graphique de polymère et son procédé de mise en œuvre |
CN116895338B (zh) * | 2023-08-08 | 2024-02-20 | 盐城师范学院 | 树形分子研究模型的改进方法及系统 |
CN117116384B (zh) * | 2023-10-20 | 2024-01-09 | 聊城高新生物技术有限公司 | 一种靶向诱导的医药分子结构生成方法 |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20190087898A (ko) * | 2018-01-17 | 2019-07-25 | 삼성전자주식회사 | 뉴럴 네트워크를 이용하여 화학 구조를 생성하는 장치 및 방법 |
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2020
- 2020-10-20 WO PCT/KR2020/014354 patent/WO2021080295A1/fr unknown
- 2020-10-20 KR KR1020200136339A patent/KR102296188B1/ko active IP Right Grant
- 2020-10-20 US US17/770,555 patent/US20220383993A1/en active Pending
- 2020-10-20 EP EP20878286.2A patent/EP4050612A4/fr active Pending
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2021
- 2021-08-25 KR KR1020210112412A patent/KR20210110539A/ko not_active Application Discontinuation
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20190087898A (ko) * | 2018-01-17 | 2019-07-25 | 삼성전자주식회사 | 뉴럴 네트워크를 이용하여 화학 구조를 생성하는 장치 및 방법 |
Non-Patent Citations (4)
Title |
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BORIS SATTAROV, IGOR I. BASKIN, DRAGOS HORVATH, GILLES MARCOU, ESBEN JANNIK BJERRUM, ALEXANDRE VARNEK: "De novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping", JOURNAL OF CHEMICAL INFORMATION AND MODELING, vol. 59, no. 3, 20 February 2019 (2019-02-20), pages 1182 - 1196, XP055731747, ISSN: 1549-9596, DOI: 10.1021/acs.jcim.8b00751 * |
JUSTIN GILMER, SAMUEL S. SCHOENHOLZ, PATRICK F. RILEY, ORIOL VINYALS, GEORGE E. DAHL: "Neural message passing for quantum chemistry", COMPUTER SCIENCE, 4 April 2017 (2017-04-04), pages 1 - 14, XP055700744 * |
KAWAI KENTARO, NAGATA NAOYA, TAKAHASHI YOSHIMASA: "De novo design of drug-like molecules by a fragment-based molecular evolutionary approach", JOURNAL OF CHEMICAL INFORMATION AND MODELING, vol. 54, no. 1, 28 December 2013 (2013-12-28), pages 49 - 56, XP055805856, ISSN: 1549-9596, DOI: 10.1021/ci400418c * |
STÅHL NICLAS, FALKMAN GÖRAN, KARLSSON ALEXANDER, MATHIASON GUNNAR, BOSTRÖM JONAS: "Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design", JOURNAL OF CHEMICAL INFORMATION AND MODELING, vol. 59, no. 7, 19 June 2019 (2019-06-19), pages 3166 - 3176, XP055803313, ISSN: 1549-9596, DOI: 10.1021/acs.jcim.9b00325 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113409898A (zh) * | 2021-06-30 | 2021-09-17 | 北京百度网讯科技有限公司 | 分子结构获取方法、装置、电子设备及存储介质 |
CN113409898B (zh) * | 2021-06-30 | 2022-05-27 | 北京百度网讯科技有限公司 | 分子结构获取方法、装置、电子设备及存储介质 |
Also Published As
Publication number | Publication date |
---|---|
EP4050612A4 (fr) | 2023-11-15 |
KR20210047262A (ko) | 2021-04-29 |
US20220383993A1 (en) | 2022-12-01 |
KR102296188B1 (ko) | 2021-09-01 |
KR20210110539A (ko) | 2021-09-08 |
EP4050612A1 (fr) | 2022-08-31 |
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